#FineTuning
22 articles tagged with #FineTuning
Fine-Tuning LLMs: A Practical Guide
Fine-tuning lets you adapt a pre-trained language model to your specific domain, style, or task — without training from scratch. This guide explains when fine-tuning is the right choice, how LoRA makes it affordable, and how to run a fine-tuning job with Hugging Face PEFT.
Fine-Tuning vs RAG: Which One Do You Actually Need?
Use RAG to give a model fresh, factual knowledge it can cite, and fine-tuning to teach it a consistent style or skill. Most real systems combine both.
Fine-Tuning vs RAG: Which Should You Use?
Use RAG to give a model fresh, factual knowledge and fine-tuning to teach it a style, format, or skill. Many systems combine both. Here is how to choose.
What Is Reinforcement Learning From Human Feedback?
RLHF fine-tunes language models using human preferences, training a reward model on ranked responses and optimizing the model to produce answers people prefer.
How GPT Models Are Trained: Pretraining to RLHF
GPT models are trained in stages: massive next-token pretraining, supervised fine-tuning on instructions, then RLHF to align outputs with human preferences.
What Is LoRA Fine-Tuning Explained
LoRA fine-tunes large models by training small adapter matrices instead of all weights, cutting memory and cost dramatically while keeping the base frozen.
AI Terminology Explained: 30 Terms Beginners Confuse
AI terminology explained in plain English: 30 terms beginners confuse, from tokens and parameters to hallucination and fine-tuning, defined clearly with examples.
How to Fine-Tune a Model Without Breaking the Bank
Learn how to fine-tune a model without breaking the bank — when to fine-tune vs prompt or use RAG, plus cheap techniques like LoRA and free ways to start.
LLM Fundamentals: How Language Models Are Built and Behave
A large language model predicts the next token from a sequence, and almost every behaviour that surprises you in production follows from that one fact. This guide connects tokenization, pretraining, fine-tuning, decoding and context limits into a single mental model you can use while debugging real systems.
Fine-Tuning Language Models: When, How and On What Data
Fine-tuning is worth reaching for when prompting cannot hold a behaviour reliably — strict output formats, house style, or a latency budget too tight for long instructions. This guide sets out the decision, the method families, the data work that actually determines quality, and how to tell a good run from a wasted one.
7 Tasks Where Fine-Tuning Beats Prompting
Fine-tuning wins where the behaviour you need is hard to describe but easy to demonstrate, where a long prompt is paid on every call, or where a smaller model must hit a latency budget. This names seven task shapes that qualify, the signals that identify them, and the cases where prompting remains the better answer.
Continued Pretraining vs Fine-Tuning for Domain Language
Continued pretraining teaches a model a domain's vocabulary and conventions from raw text; fine-tuning teaches it how to behave on a task from input-output pairs. This explains which problem each solves, how to tell them apart from your symptoms, and how to sequence them when you need both.
Fine-Tuning Loss Won't Drop: A Debugging Checklist
A fine-tuning loss curve that refuses to move almost always means the gradients are not reaching the weights you think they are. Work through label masking, tokenizer and template mismatch, learning rate, and which parameters actually have requires_grad set — in that order.
How Much Data Do You Actually Need to Fine-Tune?
Far less than most teams assume, provided the examples are narrow, internally consistent and genuinely representative. This article explains why consistency beats volume, how to test whether your dataset is sufficient by plotting performance against dataset size, and what to fix when it is not.
How to Build a Fine-Tuning Dataset From Production Logs
Production logs are the best fine-tuning data you have, because they contain the exact input distribution your model will face. This covers extracting instruction pairs from raw traffic, filtering for quality, deduplicating near-identical requests, and the permission and privacy work you cannot skip.
How to Split Train, Validation and Held-Out Sets Properly
Split fine-tuning data by grouping related examples before you split, deduplicating near-identical text across the boundary, and reserving a held-out set that is never used for any decision. This covers leakage sources specific to text data, how to detect them, and what an untouched final set is actually for.
How to Spot Overfitting Early in a Fine-Tuning Run
Overfitting shows up as validation loss rising while training loss keeps falling, and as outputs that reproduce training examples verbatim. This covers what to watch during a run, how to build a validation set that can detect the problem, and when to stop, roll back or fix the data instead.
LoRA, QLoRA and Full Fine-Tuning: Trade-offs Compared
LoRA trains small adapter matrices and leaves the base weights untouched, QLoRA does the same over a quantised base to cut memory further, and full fine-tuning updates everything. This article compares them on memory, quality ceiling, serving complexity and how easily each change can be undone.
SFT, DPO and RLHF: Preference Tuning Methods Compared
Supervised fine-tuning teaches the model to imitate good outputs, DPO teaches it to prefer one output over another from paired comparisons, and RLHF trains a reward model and optimises against it. This article compares the three on data needs, stability and what each actually changes.
Why Your Fine-Tuned Model Forgot Its General Abilities
A fine-tuned model that answers your task well but has lost its instruction-following, reasoning or other-language abilities is showing catastrophic forgetting. This explains the mechanism, how to detect it before deployment, and the mitigations - mixed data, lower adapter rank, fewer epochs and smaller learning rates.
Avoiding catastrophic forgetting and regressions when fine-tuning
A fine-tune can win your task and lose everything else. Learn why forgetting happens, mixture and rate mitigations, and the regression suite that catches it.
Building a fine-tuning dataset: format, quality and how much you need
Fine-tuning datasets fail on consistency, not size. Learn formatting, deduplication, held-out splits and quality checks that make a small set teach the behaviour.